Convolutional Neural Network and Support Vector Machine in Classification of Flower Images

Ari Peryanto, Anton Yudhana, Rusydi Umar

DOI: https://doi.org/10.23917/khif.v8i1.15531

Abstract

Flowers are among the raw materials in many industries including the pharmaceuticals and cosmetics. Manual classification of flowers requires expert judgment of a botanist and can be time consuming and inconsistent. The ability to classify flowers using computers and technology is the right solution to solve this problem. There are two algorithms that are popular in image classification, namely Convolutional Neural Network (CNN) and Support Vector Machine (SVM). CNN is one of deep neural network classification algorithms while SVM is one of machine learning algorithm. This research was an effort to determine the best performer of the two methods in flower image classification. Our observation suggests that CNN outperform SVM in flower image classification. CNN gives an accuracy of 91.6%, precision of 91.6%, recall of 91.6% and F1 Score of 91.6%.

Keywords

flower classification; convolutional neural network; support vector machine; machine learning; deep neural network

Full Text:

PDF

References

R. Waseem and W. Zenghui, “Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review,” Neural Comput., vol. 29, no. 7, pp. 2352–2449, 2017, doi: 10.1162/NECO.

N. Sharma, V. Jain, and A. Mishra, “An Analysis of Convolutional Neural Networks for Image Classification,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 377–384, 2018, doi: 10.1016/j.procs.2018.05.198.

H. Darmanto, “Pengenalan Spesies Ikan Berdasarkan Kontur Otolith,” Joined J. (Journal Informatics Educ., vol. 2, no. 1, pp. 41–59, 2019.

M. Yang and G. Thung, “Classification of Trash for Recyclability Status,” CS229Project Rep., pp. 1–6, 2016.

A. Lodh and R. Parekh, “Flower recognition system based on color and GIST features,” in 2017 Devices for Integrated Circuit (DevIC), 2017, pp. 790–794, doi: 10.1109/DEVIC.2017.8074061.

H. Almogdady, S. Manaseer, and H. Hiary, “A Flower Recognition System Based On Image Processing And Neural Networks,” Int. J. Sci. Technol. Res., vol. 7, 2018, [Online]. Available: https://www.ijstr.org/final-print/nov2018/A-Flower-Recognition-System-Based-On-Image-Processing-And-Neural-Networks.pdf.

S. N. Parvathy, N. V. Rao, S. B. S, N. Nazer, and P. A. J, “Flower Recognition System Using Cnn,” no. June, pp. 6609–6611, 2020, [Online]. Available: https://www.irjet.net/archives/V7/i6/IRJET-V7I61229.pdf.

A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation,” J. Appl. Informatics Comput., vol. 4, no. 1, pp. 45–51, 2020, doi: 10.30871/jaic.v4i1.2017.

N. S. B. Kusrorong, D. R. Sina, and N. D. Rumlaklak, “Kajian Machine Learning Dengan Komparasi Klasifikasi Prediksi Dataset Tenaga Kerja Non-Aktif,” J. Komput. Inform., vol. 7, no. 1, pp. 37–49, 2019.

A. Yudhana, Sunardi, and S. Saifullah, “Segmentation comparing eggs watermarking image and original image,” Bull. Electr. Eng. Informatics, vol. 6, no. 1, pp. 47–53, 2017, doi: 10.11591/eei.v6i1.595.

Sunardi, A. Yudhana, and S. Saifullah, “Identity analysis of egg based on digital and thermal imaging: Image processing and counting object concept,” Int. J. Electr. Comput. Eng., vol. 7, no. 1, pp. 200–208, 2017, doi: 10.11591/ijece.v7i1.pp200-208.

A. Peryanto, A. Yudhana, and R. Umar, “Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network,” FORMAT J. Ilm. Tek. Inform., vol. 8, no. 2, pp. 138–147, 2019.

K. A.ElDahshan, M. I. Youssef, E. H. Masameer, and M. A. Mustafa, “Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis based on HSV Color Space,” Int. J. Comput. Appl., vol. 90, no. 7, pp. 48–51, Mar. 2014, doi: 10.5120/15590-4426.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (3rd Edition). USA: Prentice-Hall, Inc., 2006.

Suyanto, Machine Learning Tingkat Dasar dan Lanjut. Bandung: Informatika, 2018.

M. F. Rahman, D. Alamsah, M. I. Darmawidjadja, and I. Nurma, “Klasifikasi Untuk Diagnosa Diabetes Menggunakan Metode Bayesian Regularization Neural Network (RBNN),” J. Inform., vol. 11, no. 1, pp. 36–45, 2017, doi: 10.26555/jifo.v11i1.a5452.

I. Saputra and D. Rosiyadi, “Perbandingan Kinerja Algoritma K-Nearest Neighbor , Naïve Bayes Classifier dan Support Vector Machine dalam Klasifikasi Tingkah Laku Bully pada Aplikasi Whatsapp,” Fakt. Exacta, vol. 12, no. 2, pp. 101–111, 2019.

G. Hackeling, Mastering Machine Learning with scikit-learn. Birmingham: Packt Publishing, 2014.

M. Toğaçar, B. Ergen, and Z. Cömert, “Classification of flower species by using features extracted from the intersection of feature selection methods in convolutional neural network models,” Measurement, vol. 158, p. 107703, 2020, doi: https://doi.org/10.1016/j.measurement.2020.107703.

B. R. Mete and T. Ensari, “Flower Classification with Deep CNN and Machine Learning Algorithms,” 2019 3rd Int. Symp. Multidiscip. Stud. Innov. Technol., pp. 1–5, 2019.

Article Metrics

Abstract view(s): 300 time(s)
PDF: 237 time(s)

Refbacks

  • There are currently no refbacks.